Computerized system and method for automatically generating high-quality digital content thumbnails from digital video

ABSTRACT

Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods automatically generate a thumbnail image from a frame of a video file, where the thumbnail image displays content of a selected frame determined to be high-quality and highly-relevant to the content of the video file. Frames of a video file are analyzed, and the frame that is the most contextually relevant to the video and of the highest visual quality is selected, where a thumbnail image is generated and displayed on a site or application over a network.

This application includes material that is subject to copyrightprotection. The copyright owner has no objection to the facsimilereproduction by anyone of the patent disclosure, as it appears in thePatent and Trademark Office files or records, but otherwise reserves allcopyright rights whatsoever.

FIELD

The present disclosure relates generally to improving the performance ofcontent searching, generating, providing, recommending and/or hostingcomputer systems and/or platforms by modifying the capabilities andproviding non-native functionality to such systems and/or platforms fora novel and improved framework for automatically selecting, determining,identifying and/or generating a thumbnail image from a video file, wherethe thumbnail image displays a selected image frame from the video filedetermined to be high-quality and highly-relevant to the context of thevideo file.

SUMMARY

Thumbnail images play an important role in online videos. As the mostrepresentative snapshot of a video file, thumbnails capture the essenceof a video and provide the first impression to viewers. In practice, athumbnail that captures the viewer's attention will make the video moreattractive to the viewer to click and watch the associated video.

The disclosed systems and methods implement two importantcharacteristics commonly associated with meaningful and attractivethumbnails: high relevance to video content and superior visualaesthetic quality. The disclosed systems and methods utilize these twocharacteristics in combination with one-another in order to generateimproved quality thumbnails for video.

In some embodiments, the disclosed systems and methods involve analyzingvarious visual quality and aesthetic metrics of video frames of a videofile, and performing a computerized clustering analysis to determine therelevance of the content of the frames to the video content, thus makingthe resulting thumbnails, which are generated from the video frames,more representative of the video.

In some embodiments, the selection of a quality frame as a thumbnail iscorrelated with the objective visual quality metrics of the frame'scontent, such as, for example, the frame texture and sharpness. Thus,according to some embodiments, the disclosed systems and methodsdetermine the quality of a thumbnail by analyzing the statisticalrelationship between video frames identified as potential thumbnailframes and non-thumbnail frames in terms of various image qualityfeatures.

According to some embodiments, as discussed herein, the disclosedsystems and methods automatically select a frame of a video file as athumbnail(s) that is to be displayed on a web page, application or otherform of electronic document that displays selectable and renderablecontent on the internet. As discussed in more detail below, thethumbnail(s) is automatically generated based upon an ordered orweighted, computationally determined combination of both relevance andvisual aesthetic quality analysis of the frames of the video.Accordingly, in one or more embodiments, a method is disclosed for anovel and improved framework for automatically selecting, determining,identifying and/or generating a thumbnail image from a video file, wherethe thumbnail image displays a selected frame from the video filedetermined to be high-quality and highly-relevant to the context of thevideo file.

In accordance with one or more embodiments, a non-transitorycomputer-readable storage medium is provided, the non-transitorycomputer-readable storage medium tangibly storing thereon, or havingtangibly encoded thereon, computer readable instructions that whenexecuted cause at least one processor to perform a method for a noveland improved framework for automatically selecting, determining,identifying and/or generating a thumbnail image from a video file, wherethe thumbnail image displays a selected frame from the video filedetermined to be high-quality and highly-relevant to the context of thevideo file.

In accordance with one or more embodiments, a system is provided thatcomprises one or more computing devices configured to providefunctionality in accordance with such embodiments. In accordance withone or more embodiments, functionality is embodied in steps of a methodperformed by at least one computing device. In accordance with one ormore embodiments, program code (or program logic) executed by aprocessor(s) of a computing device to implement functionality inaccordance with one or more such embodiments is embodied in, by and/oron a non-transitory computer-readable medium.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of thedisclosure will be apparent from the following description ofembodiments as illustrated in the accompanying drawings, in whichreference characters refer to the same parts throughout the variousviews. The drawings are not necessarily to scale, emphasis instead beingplaced upon illustrating principles of the disclosure:

FIG. 1 is a schematic diagram illustrating an example of a networkwithin which the systems and methods disclosed herein could beimplemented according to some embodiments of the present disclosure;

FIG. 2 depicts is a schematic diagram illustrating an example of clientdevice in accordance with some embodiments of the present disclosure;

FIG. 3 is a schematic block diagram illustrating components of anexemplary system in accordance with embodiments of the presentdisclosure;

FIG. 4 is a flowchart illustrating steps performed in accordance withsome embodiments of the present disclosure;

FIG. 5 is an example depiction of identified frames of a video file inaccordance with some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating steps performed in accordance withsome embodiments of the present disclosure; and

FIG. 7 is a block diagram illustrating the architecture of an exemplaryhardware device in accordance with one or more embodiments of thepresent disclosure.

DESCRIPTION OF EMBODIMENTS

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, certain example embodiments. Subjectmatter may, however, be embodied in a variety of different forms and,therefore, covered or claimed subject matter is intended to be construedas not being limited to any example embodiments set forth herein;example embodiments are provided merely to be illustrative. Likewise, areasonably broad scope for claimed or covered subject matter isintended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,embodiments may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

The present disclosure is described below with reference to blockdiagrams and operational illustrations of methods and devices. It isunderstood that each block of the block diagrams or operationalillustrations, and combinations of blocks in the block diagrams oroperational illustrations, can be implemented by means of analog ordigital hardware and computer program instructions. These computerprogram instructions can be provided to a processor of a general purposecomputer to alter its function as detailed herein, a special purposecomputer, ASIC, or other programmable data processing apparatus, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, implement thefunctions/acts specified in the block diagrams or operational block orblocks. In some alternate implementations, the functions/acts noted inthe blocks can occur out of the order noted in the operationalillustrations. For example, two blocks shown in succession can in factbe executed substantially concurrently or the blocks can sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

These computer program instructions can be provided to a processor of: ageneral purpose computer to alter its function to a special purpose; aspecial purpose computer; ASIC; or other programmable digital dataprocessing apparatus, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, implement the functions/acts specified in the block diagramsor operational block or blocks, thereby transforming their functionalityin accordance with embodiments herein.

For the purposes of this disclosure a computer readable medium (orcomputer-readable storage medium/media) stores computer data, which datacan include computer program code (or computer-executable instructions)that is executable by a computer, in machine readable form. By way ofexample, and not limitation, a computer readable medium may comprisecomputer readable storage media, for tangible or fixed storage of data,or communication media for transient interpretation of code-containingsignals. Computer readable storage media, as used herein, refers tophysical or tangible storage (as opposed to signals) and includeswithout limitation volatile and non-volatile, removable andnon-removable media implemented in any method or technology for thetangible storage of information such as computer-readable instructions,data structures, program modules or other data. Computer readablestorage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM,flash memory or other solid state memory technology, CD-ROM, DVD, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other physical ormaterial medium which can be used to tangibly store the desiredinformation or data or instructions and which can be accessed by acomputer or processor.

For the purposes of this disclosure the term “server” should beunderstood to refer to a service point which provides processing,database, and communication facilities. By way of example, and notlimitation, the term “server” can refer to a single, physical processorwith associated communications and data storage and database facilities,or it can refer to a networked or clustered complex of processors andassociated network and storage devices, as well as operating softwareand one or more database systems and application software that supportthe services provided by the server. Servers may vary widely inconfiguration or capabilities, but generally a server may include one ormore central processing units and memory. A server may also include oneor more mass storage devices, one or more power supplies, one or morewired or wireless network interfaces, one or more input/outputinterfaces, or one or more operating systems, such as Windows Server,Mac OS X, Unix, Linux, FreeBSD, or the like.

For the purposes of this disclosure a “network” should be understood torefer to a network that may couple devices so that communications may beexchanged, such as between a server and a client device or other typesof devices, including between wireless devices coupled via a wirelessnetwork, for example. A network may also include mass storage, such asnetwork attached storage (NAS), a storage area network (SAN), or otherforms of computer or machine readable media, for example. A network mayinclude the Internet, one or more local area networks (LANs), one ormore wide area networks (WANs), wire-line type connections, wirelesstype connections, cellular or any combination thereof. Likewise,sub-networks, which may employ differing architectures or may becompliant or compatible with differing protocols, may interoperatewithin a larger network. Various types of devices may, for example, bemade available to provide an interoperable capability for differingarchitectures or protocols. As one illustrative example, a router mayprovide a link between otherwise separate and independent LANs.

A communication link or channel may include, for example, analogtelephone lines, such as a twisted wire pair, a coaxial cable, full orfractional digital lines including T1, T2, T3, or T4 type lines,Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines(DSLs), wireless links including satellite links, or other communicationlinks or channels, such as may be known to those skilled in the art.Furthermore, a computing device or other related electronic devices maybe remotely coupled to a network, such as via a wired or wireless lineor link, for example.

For purposes of this disclosure, a “wireless network” should beunderstood to couple client devices with a network. A wireless networkmay employ stand-alone ad-hoc networks, mesh networks, Wireless LAN(WLAN) networks, cellular networks, or the like. A wireless network mayfurther include a system of terminals, gateways, routers, or the likecoupled by wireless radio links, or the like, which may move freely,randomly or organize themselves arbitrarily, such that network topologymay change, at times even rapidly.

A wireless network may further employ a plurality of network accesstechnologies, including Wi-Fi, Long Term Evolution (LTE), WLAN, WirelessRouter (WR) mesh, or 2nd, 3rd, or 4th generation (2G, 3G, or 4G)cellular technology, or the like. Network access technologies may enablewide area coverage for devices, such as client devices with varyingdegrees of mobility, for example.

For example, a network may enable RF or wireless type communication viaone or more network access technologies, such as Global System forMobile communication (GSM), Universal Mobile Telecommunications System(UMTS), General Packet Radio Services (GPRS), Enhanced Data GSMEnvironment (EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced,Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.11b/g/n,or the like. A wireless network may include virtually any type ofwireless communication mechanism by which signals may be communicatedbetween devices, such as a client device or a computing device, betweenor within a network, or the like.

A computing device may be capable of sending or receiving signals, suchas via a wired or wireless network, or may be capable of processing orstoring signals, such as in memory as physical memory states, and may,therefore, operate as a server. Thus, devices capable of operating as aserver may include, as examples, dedicated rack-mounted servers, desktopcomputers, laptop computers, set top boxes, integrated devices combiningvarious features, such as two or more features of the foregoing devices,or the like. Servers may vary widely in configuration or capabilities,but generally a server may include one or more central processing unitsand memory. A server may also include one or more mass storage devices,one or more power supplies, one or more wired or wireless networkinterfaces, one or more input/output interfaces, or one or moreoperating systems, such as Windows Server, Mac OS X, Unix, Linux,FreeBSD, or the like.

For purposes of this disclosure, a client (or consumer or user) devicemay include a computing device capable of sending or receiving signals,such as via a wired or a wireless network. A client device may, forexample, include a desktop computer or a portable device, such as acellular telephone, a smart phone, a display pager, a radio frequency(RF) device, an infrared (IR) device an Near Field Communication (NFC)device, a Personal Digital Assistant (PDA), a handheld computer, atablet computer, a phablet, a laptop computer, a set top box, a wearablecomputer, smart watch, an integrated or distributed device combiningvarious features, such as features of the forgoing devices, or the like.

A client device may vary in terms of capabilities or features. Claimedsubject matter is intended to cover a wide range of potentialvariations. For example, a simple smart phone, phablet or tablet mayinclude a numeric keypad or a display of limited functionality, such asa monochrome liquid crystal display (LCD) for displaying text. Incontrast, however, as another example, a web-enabled client device mayinclude a high-resolution screen, one or more physical or virtualkeyboards, mass storage, one or more accelerometers, one or moregyroscopes, global positioning system (GPS) or otherlocation-identifying type capability, or a display with a high degree offunctionality, such as a touch-sensitive color 2D or 3D display, forexample.

A client device may include or may execute a variety of operatingsystems, including a personal computer operating system, such as aWindows, iOS or Linux, or a mobile operating system, such as iOS,Android, or Windows Mobile, or the like.

A client device may include or may execute a variety of possibleapplications, such as a client software application enablingcommunication with other devices, such as communicating one or moremessages, such as via email, for example Yahoo!® Mail, short messageservice (SMS), or multimedia message service (MMS), for example Yahoo!Messenger®, including via a network, such as a social network,including, for example, Tumblr®, Facebook®, LinkedIn®, Twitter®,Flickr®, or Google+®, Instagram™, to provide only a few possibleexamples. A client device may also include or execute an application tocommunicate content, such as, for example, textual content, multimediacontent, or the like. A client device may also include or execute anapplication to perform a variety of possible tasks, such as browsing,searching, playing, streaming or displaying various forms of content,including locally stored or uploaded images and/or video, or games (suchas fantasy sports leagues). The foregoing is provided to illustrate thatclaimed subject matter is intended to include a wide range of possiblefeatures or capabilities.

As discussed herein, reference to an “advertisement” should beunderstood to include, but not be limited to, digital media contentembodied as a media item that provides information provided by anotheruser, service, third party, entity, and the like. Such digital adcontent can include any type of known or to be known media renderable bya computing device, including, but not limited to, video, text, audio,images, and/or any other type of known or to be known multi-media itemor object. In some embodiments, the digital ad content can be formattedas hyperlinked multi-media content that provides deep-linking featuresand/or capabilities. Therefore, while some content is referred to as anadvertisement, it is still a digital media item that is renderable by acomputing device, and such digital media item comprises content relayingpromotional content provided by a network associated party.

The principles described herein may be embodied in many different forms.By way of background, thumbnail images are displayed on a webpage, website, application interface or any other form of electronic document orinterface in order to provide a representative view of the content ofthe associated file. It is generally understood that people look atthumbnails extensively when browsing online videos, and are often acrucial decision factor in determining whether to watch a video or skipto another. Indeed, due to their importance, video producers and editorsput an increasing amount of effort into selecting meaningful andattractive thumbnails, such that when they are displayed on networksites, for example, Yahoo!® News, Sports or Finance, Tumblr®, Flickr®,and the like, they are more likely to draw the attention and curiosityof the viewing user, thereby leading to user engagement in the content.Unfortunately, manual selection of thumbnails can be cumbersome in manyscenarios. For example, user generated videos, such as the ones uploadedto Flickr® and Tumblr®, generally do not have thumbnails—e.g., videosproduced and delivered by third parties sometimes come withoutthumbnails. Selecting a thumbnail manually from even a few minutes ofthose videos can be extremely time consuming and sometimes inaccurate,making it difficult to scale.

Thus, the present disclosure provides an automatic thumbnail selectionsystem and method. The disclosed systems and methods exploit twoimportant characteristics commonly associated with meaningfulthumbnails: high relevance to video content and superior aestheticquality. In practice, selecting a thumbnail based solely on a frame'srelevance to the content of a video is insufficient because people arenaturally drawn to attractive images. For example, the frame may berelevant, but may be dark, blurry or otherwise a low quality image thatactually derails the user's interest. Superior aesthetic quality aloneis similarly insufficient because videos with attractive, but irrelevantthumbnails (e.g., clickbaits) may lead to user disappointment, whichcould ultimately result in negative reviews and harm the reputation ofvideo providers.

According to some embodiments, the disclosed systems and methodsautomatically select frames of a video file as thumbnails by analyzingvarious visual quality and aesthetic metrics of video frames, andperforming computerized clustering analysis to determine the relevanceto video content, thus making resulting thumbnails more representativeof the video. While conventional thumbnail selection systems haveaddressed similar aspects before (e.g., by alone selecting thematic,query-sensitive, and interesting thumbnails), the ability to considerboth the relevance and the attractiveness for thumbnail selection isnovel and unique to the disclosed system and methods.

The disclosed automatic thumbnail selection systems and methodsdiscussed herein are based on the understanding that a quality thumbnailis highly correlated with objective visual quality metrics, such as, forexample, the frame texture, sharpness and inclusion of interesting“things” (e.g., face, object, text, and the like), rather than thestandard photographic beauty rules, such as the Rule of Thirds. Theautomatic selection of a thumbnail leverages the relevance to videocontent and the visual attractiveness of the image frames within thevideo. Thus, the disclosed systems and methods automatically generate athumbnail(s) from a selected image frame(s) of a video based upon anordered or weighted, computationally determined combination of bothrelevance and visual aesthetic quality of the frames of the video, asdiscussed in more detail below.

For purposes of this disclosure, the selection of a thumbnail will be inlarge measure directed to analyzing image frames (referred tointerchangeably as a “video frame”) of a video file; however, it shouldnot be construed as limiting the scope of the instant application tosolely videos or the individual frames of videos, as any known or to beknown type of content, media and/or multi-media (e.g., text, audio,multi-media, RSS feeds, graphics interchange format (GIF) files, and thelike) is applicable to the disclosed systems and methods discussedherein.

For purposes of this disclosure, the term “frames” refers to the entirevideo frames of a video, while reference to a “frame” refers to anindividual frame. “Keyframes” refers to a compact and non-redundantsubset of the video frames. A “thumbnail” refers to the selected frameof a video determined to be the most attractive (or “high-quality,” usedinterchangeably) and representative (or “relevant” or “highly relevant,”used interchangeably) to the content or context of the video, asdiscussed in more detail below.

As understood by those of skill in the art, the term “high-quality”refers to an item (e.g., a frame) of digital content satisfying aquality threshold, which can be set by a user, site administrator,artist creating/capturing the content, the system, service or platformhosting the content, or some combination thereof. In a non-limitingexample, “high-quality” content can relay that the digital content isaesthetically pleasing or technically sound, in that the data associatedwith the content produces a resolution, focus, pixel quality, size,dimension, color scheme, exposure, sharpness, stillness, white balanceand the like, or some combination thereof, that satisfies the qualitythreshold. For example, video frame's quality can be determined viaimplementation of a pair-wise loss function which scores the frame'squality based on the frame's parameters or features. In anothernon-limiting example, “high-quality” can refer to the digital contentbeing of interest to a user(s), where interest (or user engagement) canbe based on the number of times a user has interacted with the content(e.g., viewed, shared, commented, downloaded, re-blogged, re-posted,favorited, liked, and the like) at or above the quality threshold.

As understood by those of skill in the art, the term or terms “relevant”or “highly-relevant” refers to an item of digital content satisfying arelevance threshold, which can be set by a user, site administrator,artist creating/capturing the content, the system, service or platformhosting the content, or some combination thereof. As discussed herein,relevancy can be quantified (or scored). For example, as discussedabove, a frame's relevancy can be determined via implementation of alogistic loss function which quantifies a frame's parameters orfeatures. In another non-limiting example, relevancy can be based on adiscounted cumulative gain (DCG) measure of ranking quality. Asunderstood by those of skill in the art, DCG can measure theeffectiveness of web search engine algorithms or related applications byanalyzing the returned results against a graded relevance scale ofcontent items in a search engine result set. DCG measures theusefulness, or gain, of a content item based on its position in theresult list. The gain is accumulated from the top of the result list tothe bottom with the gain of each result discounted at lower ranks.

The disclosed systems and methods improve a computer or networkplatform's or provider's performance of the implementation oftechnologies for multi-media analysis. Such technologies includethumbnail selection, video highlighting and summarization, andcomputation aesthetics.

Turning to conventional thumbnail selection technologies, few havespecifically addressed thumbnail selection, i.e. automatic extraction ofa single most representative frame from a video sequence. One knownsystem uses a thematic thumbnail selection framework, which chooses athumbnail frame that has the most common visual characteristics sharedwith an extra set of web images, obtained using keywords from the video.Similarly, another system uses a query-specific thumbnail selectionalgorithm that extracts a frame that is both representative of videocontent and is specific to the intent of user query by matching querywords and frame content using visual concept detectors. And, anothersystem is embodied as a query-dependent thumbnail selection method usingembedding of visual and textual signals.

While these known systems approached thumbnail selection from variousperspectives, little attention has been paid on the attractiveness ofthe thumbnails, i.e. their capability to attract the users' attentionusing visual quality and aesthetics criteria. The disclosed systems andmethods are explicitly designed to discover attractive thumbnails usingseveral visual quality metrics and computational aesthetics, asdiscussed herein. Moreover, the performances of existing frameworks areevaluated either on a small number of videos or with a rather too simpledesign of experiments (e.g., compared against either no baseline or onlyone baseline that selects the middle frame of a video). The disclosedsystems and methods can be evaluated against six baseline approaches ona vastly superior set of videos than those of the existing art—forexample, a set of 1,118 videos, which is the largest of its kind.

The goal of conventional video highlighting technologies is to find themost important segments from a video. Conventional video summarizationtechnologies goes a step further and aims to generate the “gist” of avideo by selecting a few highlight frames or segments so that theydeliver the video content in the most compact form. While the two taskshave slightly different goals from thumbnail selection, there has beenan overlap of techniques used. For example, one known system uses asparse dictionary selection approach which aims to reconstruct a videosequence from only a few “basis frames” from the video using a machinelearning statistical/regression analysis algorithm, such as, group LASSO(least absolute shrinkage and selection operator). Another conventionalapproach extends this approach into an online dictionary learningproblem. Central to these techniques is content non-redundancy in thesummary. The discloses systems and methods, however, not only removesredundant frames, but also accounts for visual quality and aesthetics toselect a single high-quality thumbnail, which none of the known systemscontemplates when analyzing media content.

Further, other known approaches: use 1) the title and description toidentify important segments in a video, 2) use animated GIFs to identifythe most GIF-able segments; and 3) use visual co-occurrence acrossmultiple videos to measure visual interestingness. The disclosed systemsand methods are different in that, while they use visual and textualcues to identify “interesting” frames, the disclosed systems and methodsdirectly assess the photographic attractiveness to identity high-qualityframes.

Conventional computational aesthetics techniques aim to automaticallyassess the photographic quality of images using visual analysistechniques. One known approach, which appeared in the field less than adecade ago, distinguishes amateur from professional photographs based onvisual features inspired by a photographic theory. Since then,computational aesthetics techniques have mainly been applied to the taskof aesthetic image classification and ranking. Other known applicationsinclude video creativity assessment, video interestingness estimation,and machine-assisted image composition and enhancement. However, none ofthe known techniques evaluate the visual aesthetic quality of videoframes for automatic thumbnail selection in accordance with therelevance of the frames, as disclosed herein.

As discussed in more detail below at least in relation to FIG. 6,according to some embodiments, information associated with or derivedfrom media including, but not limited to, videos, video frames anddetermined thumbnails associated with such videos can be used formonetization purposes and targeted advertising when providing,delivering or enabling access to such media. Providing targetedadvertising to users associated with such discovered content can lead toan increased click-through rate (CTR) of such ads and/or an increase inthe advertiser's return on investment (ROI) for serving such contentprovided by third parties (e.g., digital advertisement content providedby an advertiser, where the advertiser can be a third party advertiser,or an entity directly associated with or hosting the systems and methodsdiscussed herein).

Certain embodiments will now be described in greater detail withreference to the figures. In general, with reference to FIG. 1, a system100 in accordance with an embodiment of the present disclosure is shown.FIG. 1 shows components of a general environment in which the systemsand methods discussed herein may be practiced. Not all the componentsmay be required to practice the disclosure, and variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of the disclosure. As shown, system 100 of FIG.1 includes local area networks (“LANs”)/wide area networks(“WANs”)—network 105, wireless network 110, mobile devices (clientdevices) 102-104 and client device 101. FIG. 1 additionally includes avariety of servers, such as content server 106, application (or “App”)server 108, search server 120 and advertising (“ad”) server 130.

One embodiment of mobile devices 102-104 is described in more detailbelow. Generally, however, mobile devices 102-104 may include virtuallyany portable computing device capable of receiving and sending a messageover a network, such as network 105, wireless network 110, or the like.Mobile devices 102-104 may also be described generally as client devicesthat are configured to be portable. Thus, mobile devices 102-104 mayinclude virtually any portable computing device capable of connecting toanother computing device and receiving information. Such devices includemulti-touch and portable devices such as, cellular telephones, smartphones, display pagers, radio frequency (RF) devices, infrared (IR)devices, Personal Digital Assistants (PDAs), handheld computers, laptopcomputers, wearable computers, smart watch, tablet computers, phablets,integrated devices combining one or more of the preceding devices, andthe like. As such, mobile devices 102-104 typically range widely interms of capabilities and features. For example, a cell phone may have anumeric keypad and a few lines of monochrome LCD display on which onlytext may be displayed. In another example, a web-enabled mobile devicemay have a touch sensitive screen, a stylus, and an HD display in whichboth text and graphics may be displayed.

A web-enabled mobile device may include a browser application that isconfigured to receive and to send web pages, web-based messages, and thelike. The browser application may be configured to receive and displaygraphics, text, multimedia, and the like, employing virtually any webbased language, including a wireless application protocol messages(WAP), and the like. In one embodiment, the browser application isenabled to employ Handheld Device Markup Language (HDML), WirelessMarkup Language (WML), WMLScript, JavaScript, Standard GeneralizedMarkup Language (SMGL), HyperText Markup Language (HTML), eXtensibleMarkup Language (XML), and the like, to display and send a message.

Mobile devices 102-104 also may include at least one client applicationthat is configured to receive content from another computing device. Theclient application may include a capability to provide and receivetextual content, graphical content, audio content, and the like. Theclient application may further provide information that identifiesitself, including a type, capability, name, and the like. In oneembodiment, mobile devices 102-104 may uniquely identify themselvesthrough any of a variety of mechanisms, including a phone number, MobileIdentification Number (MIN), an electronic serial number (ESN), or othermobile device identifier.

In some embodiments, mobile devices 102-104 may also communicate withnon-mobile client devices, such as client device 101, or the like. Inone embodiment, such communications may include sending and/or receivingmessages, searching for, viewing and/or sharing photographs, audioclips, video clips, or any of a variety of other forms ofcommunications. Client device 101 may include virtually any computingdevice capable of communicating over a network to send and receiveinformation. The set of such devices may include devices that typicallyconnect using a wired or wireless communications medium such as personalcomputers, multiprocessor systems, microprocessor-based or programmableconsumer electronics, network PCs, or the like. Thus, client device 101may also have differing capabilities for displaying navigable views ofinformation.

Client devices 101-104 computing device may be capable of sending orreceiving signals, such as via a wired or wireless network, or may becapable of processing or storing signals, such as in memory as physicalmemory states, and may, therefore, operate as a server. Thus, devicescapable of operating as a server may include, as examples, dedicatedrack-mounted servers, desktop computers, laptop computers, set topboxes, integrated devices combining various features, such as two ormore features of the foregoing devices, or the like.

Wireless network 110 is configured to couple mobile devices 102-104 andits components with network 105. Wireless network 110 may include any ofa variety of wireless sub-networks that may further overlay stand-alonead-hoc networks, and the like, to provide an infrastructure-orientedconnection for mobile devices 102-104. Such sub-networks may includemesh networks, Wireless LAN (WLAN) networks, cellular networks, and thelike.

Network 105 is configured to couple content server 106, applicationserver 108, or the like, with other computing devices, including, clientdevice 101, and through wireless network 110 to mobile devices 102-104.Network 105 is enabled to employ any form of computer readable media forcommunicating information from one electronic device to another. Also,network 105 can include the Internet in addition to local area networks(LANs), wide area networks (WANs), direct connections, such as through auniversal serial bus (USB) port, other forms of computer-readable media,or any combination thereof. On an interconnected set of LANs, includingthose based on differing architectures and protocols, a router acts as alink between LANs, enabling messages to be sent from one to another,and/or other computing devices.

Within the communications networks utilized or understood to beapplicable to the present disclosure, such networks will employ variousprotocols that are used for communication over the network. Signalpackets communicated via a network, such as a network of participatingdigital communication networks, may be compatible with or compliant withone or more protocols. Signaling formats or protocols employed mayinclude, for example, TCP/IP, UDP, QUIC (Quick UDP Internet Connection),DECnet, NetBEUI, IPX, APPLETALK™, or the like. Versions of the InternetProtocol (IP) may include IPv4 or IPv6. The Internet refers to adecentralized global network of networks. The Internet includes localarea networks (LANs), wide area networks (WANs), wireless networks, orlong haul public networks that, for example, allow signal packets to becommunicated between LANs. Signal packets may be communicated betweennodes of a network, such as, for example, to one or more sites employinga local network address. A signal packet may, for example, becommunicated over the Internet from a user site via an access nodecoupled to the Internet. Likewise, a signal packet may be forwarded vianetwork nodes to a target site coupled to the network via a networkaccess node, for example. A signal packet communicated via the Internetmay, for example, be routed via a path of gateways, servers, etc. thatmay route the signal packet in accordance with a target address andavailability of a network path to the target address.

According to some embodiments, the present disclosure may also beutilized within or accessible to an electronic social networking site. Asocial network refers generally to an electronic network of individuals,such as, but not limited to, acquaintances, friends, family, colleagues,or co-workers, that are coupled via a communications network or via avariety of sub-networks. Potentially, additional relationships maysubsequently be formed as a result of social interaction via thecommunications network or sub-networks. In some embodiments, multi-modalcommunications may occur between members of the social network.Individuals within one or more social networks may interact orcommunication with other members of a social network via a variety ofdevices. Multi-modal communication technologies refers to a set oftechnologies that permit interoperable communication across multipledevices or platforms, such as cell phones, smart phones, tabletcomputing devices, phablets, personal computers, televisions, set-topboxes, SMS/MMS, email, instant messenger clients, forums, socialnetworking sites, or the like.

In some embodiments, the disclosed networks 110 and/or 105 may comprisea content distribution network(s). A “content delivery network” or“content distribution network” (CDN) generally refers to a distributedcontent delivery system that comprises a collection of computers orcomputing devices linked by a network or networks. A CDN may employsoftware, systems, protocols or techniques to facilitate variousservices, such as storage, caching, communication of content, orstreaming media or applications. A CDN may also enable an entity tooperate or manage another's site infrastructure, in whole or in part.

The content server 106 may include a device that includes aconfiguration to provide content via a network to another device. Acontent server 106 may, for example, host a site or service, such as aphoto sharing site/service (e.g., Tumblr®), an email platform or socialnetworking site, a search platform or site, or a personal user site(such as a blog, vlog, online dating site, and the like) and the like. Acontent server 106 may also host a variety of other sites, including,but not limited to business sites, educational sites, dictionary sites,encyclopedia sites, wikis, financial sites, government sites, and thelike. Devices that may operate as content server 106 include personalcomputers desktop computers, multiprocessor systems,microprocessor-based or programmable consumer electronics, network PCs,servers, and the like.

Content server 106 can further provide a variety of services thatinclude, but are not limited to, streaming and/or downloading mediaservices, search services, email services, photo services, web services,social networking services, news services, third-party services, audioservices, video services, instant messaging (IM) services, SMS services,MMS services, FTP services, voice over IP (VOIP) services, or the like.Such services, for example a mail application and/or email-platform, canbe provided via the application server 108, whereby a user is able toutilize such service upon the user being authenticated, verified oridentified by the service. Examples of content may include videos, text,audio, images, or the like, which may be processed in the form ofphysical signals, such as electrical signals, for example, or may bestored in memory, as physical states, for example.

An ad server 130 comprises a server that stores online advertisementsfor presentation to users. “Ad serving” refers to methods used to placeonline advertisements on websites, in applications, or other placeswhere users are more likely to see them, such as during an onlinesession or during computing platform use, for example. Variousmonetization techniques or models may be used in connection withsponsored advertising, including advertising associated with user. Suchsponsored advertising includes monetization techniques includingsponsored search advertising, non-sponsored search advertising,guaranteed and non-guaranteed delivery advertising, adnetworks/exchanges, ad targeting, ad serving and ad analytics. Suchsystems can incorporate near instantaneous auctions of ad placementopportunities during web page creation, (in some cases in less than 500milliseconds) with higher quality ad placement opportunities resultingin higher revenues per ad. That is advertisers will pay higheradvertising rates when they believe their ads are being placed in oralong with highly relevant content that is being presented to users.Reductions in the time needed to quantify a high quality ad placementoffers ad platforms competitive advantages. Thus higher speeds and morerelevant context detection improve these technological fields.

For example, a process of buying or selling online advertisements mayinvolve a number of different entities, including advertisers,publishers, agencies, networks, or developers. To simplify this process,organization systems called “ad exchanges” may associate advertisers orpublishers, such as via a platform to facilitate buying or selling ofonline advertisement inventory from multiple ad networks. “Ad networks”refers to aggregation of ad space supply from publishers, such as forprovision en masse to advertisers. For web portals like Yahoo!®,advertisements may be displayed on web pages or in apps resulting from auser-defined search based at least in part upon one or more searchterms. Advertising may be beneficial to users, advertisers or webportals if displayed advertisements are relevant to interests of one ormore users. Thus, a variety of techniques have been developed to inferuser interest, user intent or to subsequently target relevantadvertising to users. One approach to presenting targeted advertisementsincludes employing demographic characteristics (e.g., age, income,gender, occupation, etc.) for predicting user behavior, such as bygroup. Advertisements may be presented to users in a targeted audiencebased at least in part upon predicted user behavior(s).

Another approach includes profile-type ad targeting. In this approach,user profiles specific to a user may be generated to model userbehavior, for example, by tracking a user's path through a web site ornetwork of sites, and compiling a profile based at least in part onpages or advertisements ultimately delivered. A correlation may beidentified, such as for user purchases, for example. An identifiedcorrelation may be used to target potential purchasers by targetingcontent or advertisements to particular users. During presentation ofadvertisements, a presentation system may collect descriptive contentabout types of advertisements presented to users. A broad range ofdescriptive content may be gathered, including content specific to anadvertising presentation system. Advertising analytics gathered may betransmitted to locations remote to an advertising presentation systemfor storage or for further evaluation. Where advertising analyticstransmittal is not immediately available, gathered advertising analyticsmay be stored by an advertising presentation system until transmittal ofthose advertising analytics becomes available.

Servers 106, 108, 120 and 130 may be capable of sending or receivingsignals, such as via a wired or wireless network, or may be capable ofprocessing or storing signals, such as in memory as physical memorystates. Devices capable of operating as a server may include, asexamples, dedicated rack-mounted servers, desktop computers, laptopcomputers, set top boxes, integrated devices combining various features,such as two or more features of the foregoing devices, or the like.Servers may vary widely in configuration or capabilities, but generally,a server may include one or more central processing units and memory. Aserver may also include one or more mass storage devices, one or morepower supplies, one or more wired or wireless network interfaces, one ormore input/output interfaces, or one or more operating systems, such asWindows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.

In some embodiments, users are able to access services provided byservers 106, 108, 120 and/or 130. This may include in a non-limitingexample, authentication servers, search servers, email servers, socialnetworking services servers, SMS servers, IM servers, MMS servers,exchange servers, photo-sharing services servers, and travel servicesservers, via the network 105 using their various devices 101-104. Insome embodiments, applications, such as a photo sharing/user-generatedcontent (UGC) application (e.g., Flickr®, Tumblr®, and the like), astreaming video application (e.g., Netflix®, Hulu®, iTunes®, AmazonPrime®, HBO Go®, and the like), blog, photo or social networkingapplication (e.g., Facebook®, Twitter® and the like), search application(e.g., Yahoo!® Search), a mail or messaging application (e.g., Yahoo!®Mail, Yahoo!® Messenger), and the like, can be hosted by the applicationserver 108 (or content server 106, search server 120 and the like).Thus, the application server 108 can store various types of applicationsand application related information including application data and userprofile information (e.g., identifying and behavioral informationassociated with a user). It should also be understood that contentserver 106 can also store various types of data related to the contentand services provided by content server 106 in an associated contentdatabase 107, as discussed in more detail below. Embodiments exist wherethe network 105 is also coupled with/connected to a Trusted SearchServer (TSS) which can be utilized to render content in accordance withthe embodiments discussed herein. Embodiments exist where the TSSfunctionality can be embodied within servers 106, 108, 120 and/or 130.

Moreover, although FIG. 1 illustrates servers 106, 108, 120 and 130 assingle computing devices, respectively, the disclosure is not solimited. For example, one or more functions of servers 106, 108, 120and/or 130 may be distributed across one or more distinct computingdevices. Moreover, in one embodiment, servers 106, 108, 120 and/or 130may be integrated into a single computing device, without departing fromthe scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating a client device showing anexample embodiment of a client device that may be used within thepresent disclosure. Client device 200 may include many more or lesscomponents than those shown in FIG. 2. However, the components shown aresufficient to disclose an illustrative embodiment for implementing thepresent disclosure. Client device 200 may represent, for example, clientdevices discussed above in relation to FIG. 1.

As shown in the figure, Client device 200 includes a processing unit(CPU) 222 in communication with a mass memory 230 via a bus 224. Clientdevice 200 also includes a power supply 226, one or more networkinterfaces 250, an audio interface 252, a display 254, a keypad 256, anilluminator 258, an input/output interface 260, a haptic interface 262,an optional global positioning systems (GPS) receiver 264 and acamera(s) or other optical, thermal or electromagnetic sensors 266.Device 200 can include one camera/sensor 266, or a plurality ofcameras/sensors 266, as understood by those of skill in the art. Thepositioning of the camera(s)/sensor(s) 266 on device 200 can change perdevice 200 model, per device 200 capabilities, and the like, or somecombination thereof.

Power supply 226 provides power to Client device 200. A rechargeable ornon-rechargeable battery may be used to provide power. The power mayalso be provided by an external power source, such as an AC adapter or apowered docking cradle that supplements and/or recharges a battery.

Client device 200 may optionally communicate with a base station (notshown), or directly with another computing device. Network interface 250includes circuitry for coupling Client device 200 to one or morenetworks, and is constructed for use with one or more communicationprotocols and technologies as discussed above. Network interface 250 issometimes known as a transceiver, transceiving device, or networkinterface card (NIC).

Audio interface 252 is arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 252 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others and/or generate an audio acknowledgementfor some action. Display 254 may be a liquid crystal display (LCD), gasplasma, light emitting diode (LED), or any other type of display usedwith a computing device. Display 254 may also include a touch sensitivescreen arranged to receive input from an object such as a stylus or adigit from a human hand.

Keypad 256 may comprise any input device arranged to receive input froma user. For example, keypad 256 may include a push button numeric dial,or a keyboard. Keypad 256 may also include command buttons that areassociated with selecting and sending images. Illuminator 258 mayprovide a status indication and/or provide light. Illuminator 258 mayremain active for specific periods of time or in response to events. Forexample, when illuminator 258 is active, it may backlight the buttons onkeypad 256 and stay on while the client device is powered. Also,illuminator 258 may backlight these buttons in various patterns whenparticular actions are performed, such as dialing another client device.Illuminator 258 may also cause light sources positioned within atransparent or translucent case of the client device to illuminate inresponse to actions.

Client device 200 also comprises input/output interface 260 forcommunicating with external devices, such as a headset, or other inputor output devices not shown in FIG. 2. Input/output interface 260 canutilize one or more communication technologies, such as USB, infrared,Bluetooth™, or the like. Haptic interface 262 is arranged to providetactile feedback to a user of the client device. For example, the hapticinterface may be employed to vibrate client device 200 in a particularway when the Client device 200 receives a communication from anotheruser.

Optional GPS transceiver 264 can determine the physical coordinates ofClient device 200 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 264 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), E-OTD, CI, SAI, ETA, BSS or thelike, to further determine the physical location of Client device 200 onthe surface of the Earth. It is understood that under differentconditions, GPS transceiver 264 can determine a physical location withinmillimeters for Client device 200; and in other cases, the determinedphysical location may be less precise, such as within a meter orsignificantly greater distances. In one embodiment, however, Clientdevice may through other components, provide other information that maybe employed to determine a physical location of the device, includingfor example, a MAC address, Internet Protocol (IP) address, or the like.

Mass memory 230 includes a RAM 232, a ROM 234, and other storage means.Mass memory 230 illustrates another example of computer storage mediafor storage of information such as computer readable instructions, datastructures, program modules or other data. Mass memory 230 stores abasic input/output system (“BIOS”) 240 for controlling low-leveloperation of Client device 200. The mass memory also stores an operatingsystem 241 for controlling the operation of Client device 200. It willbe appreciated that this component may include a general purposeoperating system such as a version of UNIX, or LINUX™, or a specializedclient communication operating system such as Windows Client™, or theSymbian® operating system. The operating system may include, orinterface with a Java virtual machine module that enables control ofhardware components and/or operating system operations via Javaapplication programs.

Memory 230 further includes one or more data stores, which can beutilized by Client device 200 to store, among other things, applications242 and/or other data. For example, data stores may be employed to storeinformation that describes various capabilities of Client device 200.The information may then be provided to another device based on any of avariety of events, including being sent as part of a header during acommunication, sent upon request, or the like. At least a portion of thecapability information may also be stored on a disk drive or otherstorage medium (not shown) within Client device 200.

Applications 242 may include computer executable instructions which,when executed by Client device 200, transmit, receive, and/or otherwiseprocess audio, video, images, and enable telecommunication with a serverand/or another user of another client device. Other examples ofapplication programs or “apps” in some embodiments include browsers,calendars, contact managers, task managers, transcoders, photomanagement, database programs, word processing programs, securityapplications, spreadsheet programs, games, search programs, and soforth. Applications 242 may further include search client 245 that isconfigured to send, to receive, and/or to otherwise process a searchquery and/or search result using any known or to be known communicationprotocols. Although a single search client 245 is illustrated it shouldbe clear that multiple search clients may be employed. For example, onesearch client may be configured to enter a search query message, whereanother search client manages search results, and yet another searchclient is configured to manage serving advertisements, IMs, emails, andother types of known messages, or the like.

Having described the components of the general architecture employedwithin the disclosed systems and methods, the components' generaloperation with respect to the disclosed systems and methods will now bedescribed below.

FIG. 3 is a block diagram illustrating the components for performing thesystems and methods discussed herein. FIG. 3 includes a thumbnail engine300, network 315 and database 320. The thumbnail engine 300 can be aspecial purpose machine or processor and could be hosted by anapplication server, content server, social networking server, webserver, search server, content provider, email service provider, adserver, user's computing device, and the like, or any combinationthereof.

According to some embodiments, thumbnail engine 300 can be embodied as astand-alone application that executes on a user device. In someembodiments, the thumbnail engine 300 can function as an applicationinstalled on the user's device, and in some embodiments, suchapplication can be a web-based application accessed by the user deviceover a network. In some embodiments, the thumbnail engine 300 can beinstalled as an augmenting script, program or application to anothermedia and/or content hosting/serving application, such as, for example,Yahoo!® Search, Yahoo!® Mail, Flickr®, Tumblr®, Twitter®, Instagram®,SnapChat®, Facebook®, Amazon®, YouTube® and the like.

The database 320 can be any type of database or memory, and can beassociated with a content server on a network (e.g., content server 106,search server 120 or application server 108 from FIG. 1) or a user'sdevice (e.g., device 101-104 or device 200 from FIGS. 1-2). Database 320comprises a dataset of content items (e.g., video files, multi-mediafiles, images and the like), user data and associated user metadata.Such information can be stored in the database 320 independently and/oras a linked or associated dataset. It should be understood that thevideo data (and metadata) in the database 320 can be any type of videoinformation and type, whether known or to be known, without departingfrom the scope of the present disclosure.

Database 320 comprises a dataset of data and metadata associated withlocal and/or network information related to users, services,applications, user-generated content, third party provided content andthe like. Such information can be stored and indexed in the database 320independently and/or as a linked or associated dataset. As discussedabove, it should be understood that the data (and metadata) in thedatabase 320 can be any type of information and type, whether known orto be known, without departing from the scope of the present disclosure.

For purposes of the present disclosure, as discussed above, videos(which are stored and located in database 320) as a whole are discussedwithin some embodiments; however, it should not be construed to limitthe applications of the systems and methods discussed herein. Indeed,while reference is made throughout the instant disclosure to videos,other forms of known or to be known content types/formats and associatedinformation, including for example graphics interchange files (GIFs) orother short form video media files, text, audio, multi-media, RSS feedinformation can be used without departing from the scope of the instantapplication, which can thereby be communicated and/or accessed andprocessed by the thumbnail engine 300 according to the systems andmethods discussed herein.

According to some embodiments, database 320 can store data for users,i.e., user data. According to some embodiments, the stored user data caninclude, but is not limited to, information associated with a user'sprofile, user interests, user behavioral information, user attributes,user preferences or settings, user demographic information, userlocation information, user biographic information, and the like, or somecombination thereof. In some embodiments, the user data can alsoinclude, for purposes rendering and/or displaying content, user deviceinformation, including, but not limited to, device identifyinginformation, device capability information, voice/data carrierinformation, Internet Protocol (IP) address, applications installed orcapable of being installed or executed on such device, and/or any, orsome combination thereof. It should be understood that the data (andmetadata) in the database 320 can be any type of information related toa user, content, a device, an application, a service provider, a contentprovider, whether known or to be known, without departing from the scopeof the present disclosure.

According to some embodiments, database 320 can comprise informationassociated with content providers, such as, but not limited to, contentgenerating and hosting sites or providers that enable users to searchfor, upload, download, share, edit or otherwise avail users to content(e.g., Yahoo!® Search, Yahoo!® Mobile applications, Yahoo!® Mail,Flickr®, Tumblr®, Twitter®, Instagram®, SnapChat®, Facebook®, Amazon®,YouTube®, and the like). In some embodiments, database 320 can comprisedata and metadata associated with such content information from oneand/or an assortment of media hosting sites.

In some embodiments, the information stored in database 320 can berepresented as an n-dimensional vector (or feature vector) for eachstored data/metadata item, where the information associated with, forexample, the stored videos or frames of videos can corresponds to anode(s) on the vector. As such, database 320 can store and index contentinformation in database 320 as linked set of data and metadata, wherethe data and metadata relationship can be stored as the n-dimensionalvector discussed above. Such storage can be realized through any knownor to be known vector or array storage, including but not limited to, ahash tree, queue, stack, VList, or any other type of known or to beknown dynamic memory allocation technique or technology.

While the discussion of some embodiments involves vector analysis ofstored information, as discussed above, the information can be analyzed,stored and indexed according to any known or to be known computationalanalysis technique or algorithm, such as, but not limited to, word2vecanalysis, cluster analysis, data mining, Bayesian network analysis,Hidden Markov models, artificial neural network analysis, logical modeland/or tree analysis, and the like.

In some embodiments, database 320 can be a single database housinginformation associated with one or more services and/or contentproviders, and in some embodiments, database 320 can be configured as alinked set of data stores that provides such information, as eachdatastore in the set is associated with and/or unique to a specificservice and/or content provider.

As discussed above, with reference to FIG. 1, the network 315 can be anytype of network such as, but not limited to, a wireless network, a localarea network (LAN), wide area network (WAN), the Internet, or acombination thereof. The network 315 facilitates connectivity of thethumbnail engine 300, and the database of stored resources 320. Indeed,as illustrated in FIG. 3, the thumbnail engine 300 and database 320 canbe directly connected by any known or to be known method of connectingand/or enabling communication between such devices and resources.

The principal processor, server, or combination of devices thatcomprises hardware programmed in accordance with the special purposefunctions herein is referred to for convenience as thumbnail engine 300,and includes video module 302, frame filtering module 304, keyframeextraction module 306 and thumbnail selection module 308. It should beunderstood that the engine(s) and modules discussed herein arenon-exhaustive, as additional or fewer engines and/or modules (orsub-modules) may be applicable to the embodiments of the systems andmethods discussed. The operations, configurations and functionalities ofeach module, and their role within embodiments of the present disclosurewill be discussed below.

Turning to FIG. 4, Process 400 details steps performed in accordancewith some embodiments of the present disclosure for automaticallyselecting relevant, high-quality image frames from videos to be used asthumbnails when displaying a video file, for example, on a website.

As discussed herein, the key steps of Process 400 involve: 1) framefiltering (Step 404); 2) keyframe extraction (Step 406); and 3)thumbnail selection (Step 408). As evidenced from the below discussion,the complexity of computational analysis that occurs in each stepincreases as Process 400 proceeds through the analysis of the videodata. That is, the frame filtering in Step 404 of Process 400 involvesidentifying low-quality images, for example, which requires a relativelylow computational complexity compared to the keyframe extraction of Step406 and thumbnail selection of Step 408. Similarly, the keyframeextraction in Step 406 involves performing, for example, identifyingduplicate or “near-duplicate” images, which has a lower computationalcomplexity than that performed in Step 408's thumbnail selection, whichinvolves the selection of representative and attractive frames of avideo file. Process 400 is configured in such a manner so the thumbnailengine 300's time and memory usage is maximized for the tasks thatrequire most computational resources. Indeed, the thumbnail engine 300is designed to engage more computational power from its associateddevice(s) as it performs the more sophisticated tasks or processes.

Process 400 beings with Step 402 where an input video is identified orreceived. Step 402 is performed by the video module 302 of the thumbnailengine 300. In some embodiments, the input video can be a video filethat is uploaded to a media file hosting site, service or platform, suchas, for example, Tumblr®, Flickr®, Facebook®, Yahoo!® eSports, News,Sports, Finance and the like, YouTube®, Amazon®, and the like. The videofile can be a user-generated content (UGC) file, a provider provided orgenerated file, or any type of third party provided or generated file.While the discussion herein focuses on a video file being uploaded, anyaction can trigger the identification of a video file for whichautomatic thumbnail analysis can be performed, as discussed herein, suchas, for example, downloading a video, capturing a video, receiving avideo (e.g., via sharing), posting a video (e.g., to a social networkingplatform), and the like, without departing from the scope of the instantdisclosure.

In Step 404, after an input video is identified, the frame filteringmodule 304 is applied to the input video. As discussed herein, theanalysis of the frames of the input video using the frame filteringmodule 304 results in the identification of a qualityvalue/metric/determination of the frames as well as the types of framesin the video. As discussed below, the result of the frame filteringoccurring in Step 404 includes the discarding of “low-quality” framesand using the remaining frames in the subsequent keyframe analysis ofStep 406, as discussed in more detail below. “Low-quality” frames caninclude, for example, frames depicting dark, blurry, uniform content andthe like, or transition frames (e.g., cut, fade, dissolve, wipe, and thelike).

As will be understood by those of skill in the art, processing of theframes (i.e., all of the frames) in a video can be quite time consumingand computationally burdensome to a computing system. Many conventionalvideo processing systems reduce the number of frames by sub sampling ata uniform time interval. However, for the purpose of the disclosedthumbnail selection Process 400 discussed herein, such subsamplingmethodology can actually lead to discarding of frames that would beideal as a thumbnail, therefore making it undesirable for the disclosedtechniques. As discussed herein and in more detail below, high-qualitythumbnails must possess certain aesthetic qualities, e.g., brightness,sharpness, colorfulness, stillness, and the like. Avoiding thesubsampling steps generic to conventional systems and applying the framefiltering module 304 in Step 404 allows the thumbnail engine 300 toreduce the number of frames by filtering out low-quality frames therebyimproving processing efficiency in the later stages.

Step 404 involves parsing the video file in order to identify the framesof the video. Step 404 then involves detecting and filtering out apredetermined type of low quality frames, as discussed herein. Forexample, and for purposes of this discussion, the predetermined types offrames includes three (3) frame types: dark, blurry and uniform-colored;however, such discussion should not be limited as any other type ofknown or to be known type can be utilized without departing from thescope of the instant application.

In some embodiments, “dark frames” are filtered out by analyzing eachframe of a video file by computing a relative luminance, as follows:Luminance(I _(rgb))=0.2126Ir+0.7152I _(g)+0.0722I _(b)  (Eq. 1),

where “rgb” refers to the RGB color space. The luminance is then subjectto thresholding it using an empirically chosen value.

In some embodiments, “blurry frames” are filtered out in a similarmanner by computing the sharpness, as follows:Sharpness(I _(gray))=√((Δ_(x) Igray)²+(Δ_(y) Igray)²)  (Eq. 2).

In some embodiments, to filter out “uniform-colored” frames, anormalized intensity histogram is first computed for an image displayedwithin a frame, resulting in values of intensity, the values are sortedin descending order, a cumulative distribution at a top percentage(e.g., 5%) bins is computed, and then these values are thresholded withan empirically chosen value:Uniformity(I _(gray))=∫₀ ^(5%) cdf(sort(hist(I _(gray))))dp  (Eq. 3).

Step 404 also involves detecting and filtering out frames determined tobe transition or transitioning frames. In some embodiments, such framescould also be identified as “low-quality” using the above analysis.

The identification and determination of transitioning frames occurringin Step 404 involves performing shot boundary detection. In someembodiments, the frame filtering module 304 applies a computerized shotboundary detection technique or algorithm that determines which frameswithin the video are transition frames. For example, the module 304 canemploy a shot boundary detection technique using a methodology thatcomputes the edge change ratio between two consecutive frames anddetects a boundary with thresholding. However, implementation of suchtechnique should not be construed as limiting, as any type of known orto be known shot boundary detection system, algorithm, or technology canbe utilized herein without departing from the scope of the instantdisclosure.

Step 404's determination of low-quality and/or transitioning frames fromthe frames of the input video file results in the removal of such framessuch that the remaining frames are not identified as low-quality (theyare high-quality) and are not transition frames (they correspond toscenes or shots in the video).

In Step 406, the keyframe extraction module 306 is applied to theremaining frames—i.e., the frames that remain after application of theframe filtering module 304, from Step 404—whereby the result of Step 406involves the identification and removal (e.g., discarding) of duplicateor “near-duplicate” frames (where “near-duplicate” frames comprisecontent that is similar in its parameters at or above a thresholdvalue).

As understood by those of skill in the art, a video sequence, by nature,has many near duplicate frames. The disclosed systems and methods, viathe performance of Step 406 by the keyframe extraction module 306,filters out the duplicate frames via keyframe extraction, as discussedherein. Step 406 can perform keyframe extraction via any known or to beknown technique or algorithm for extracting keyframes in frames ofvideo. In some embodiments, the keyframe extraction module 306 employs aclustering analysis technique or algorithm. Cluster analysis clustersframes by their visual similarity, and selects the most representativeframes, one per cluster, by selecting a frame closest to the centroid(e.g., using the k-means algorithm) or the medoid (using the k-medoidsalgorithm) of samples within each cluster.

Such computational steps are performed by the keyframe extraction module306 using image aesthetics to select a frame from each cluster(especially using the “stillness” within an image of a frame, asdiscussed below). Such selection can be based on the identification of ablurring artifact caused by motion compensation during videocompression. The keyframe extraction module 306 identifies blurredframes, via, for example, the cluster analysis and image aestheticstechniques discussed herein, when there is high motion energy detectedfrom such analysis. In other words, sharper, higher quality keyframesare associated with frames that have determined low motion energy.

According to some embodiments, the keyframe extraction occurring in Step406 involves determining a feature representation for the remainingframes (e.g., translating the data and metadata of the frames into afeature vector, for example). In some embodiments, such featurerepresentations can be determined by the keyframe extraction module 306using, or applying software defining, any known or to be known histogramanalysis or vector analysis, as discussed above, or any known or to beknown deep learning architecture or algorithm, such as, but not limitedto, deep neural networks, artificial neural networks (ANNs),convolutional neural networks (CNNs), deep belief networks and the like.

According to some embodiments, since the goal of the keyframe analysisof Step 406 is to identify duplicate frames using the leastcomputational energy as possible, keyframe extraction module 306 canemploy de-duplication software that employs, for example, a color andedge histogram technique. While such technique is discussed herein forperforming Step 406, it should not be construed as limiting, as anyknown or to be known computational technique for determining parametersof a frame and determining duplicates based on such parameters can beused herein, as discussed above, without departing from the scope of theinstant application.

In some embodiments, Step 406 involves computing two types of features:a pyramid of HSV (hue, saturation, and value) histograms with 128 binsper channel, and a pyramid of edge orientations and magnitudes with 30bins for each. The features are computed over a two-level spatialpyramid (five regions), resulting in a 2,220 dimensional feature vector.In some embodiments, the features of such vector are compared and theduplicate images are identified and discarded. Such comparison caninvolve, for example, comparing the values of the feature vector to athreshold and determining which values satisfy the threshold, and basedon such threshold comparison, the frames associated with such featurescan be removed or remain. In some embodiments, the threshold cancorrespond to a predetermined distance between features in therepresentation/vector.

Step 406 further involves a subshot identification from the remainingframes of the video file. The frames output from Step 404 corresponds tosingle scenes or “shots” as the transition and low-quality frames werediscarded. In some embodiments, a shot, and subsequent subshot can bedetermined based on the feature representation discussed above. Forexample, a shot can correspond to a set of features in the featurerepresentation, or set of features within (or across) a dimension of thefeature representation. Similarly, a subshot can be identified from aset of features whereby the features correspond to one another at orwithin a threshold value. In some embodiments, subshots (or sub-scenes)can be identified by extracting one keyframe per shot respective to acontinuous block of the remaining frames (corresponding to a single“shot” or “scene”). Step 406 involves the keyframe extraction module 306identifying subshots by clustering the remaining frames using thek-means algorithm, where the number of clusters is set according to thenumber of shots in a video. A subshot within a shot is then identifiedas a continuous block of frames with the same cluster, and a keyframefor each subshot is extracted, as discussed above.

For each frame in the identified subshots, the keyframe extractionmodule 306 computes a “stillness” value as an inverse of the sum-squaredpixel-wise frame difference value between two time-consecutive frames.In a similar manner discussed above, the “stillness” metric representsmotion energy of a frame. The “stillness” value corresponds to themotion in the frame, where the lower the value, the more “still” thecontent in the image frame.

For example, as depicted in FIG. 5, two images from a subshot aredisplayed, and they both correspond to frames of a woman “playing”tennis. The shot 502 depicts a frame of the shot swinging her racquet;while the shot 504 depicts her still in the frame. The shot 504 has alower minimum frame difference value—“stillness” value—therefore, shot504 would remain after keyframe analysis of Step 406, and shot 502,which depicts high energy movement (and is blurry), would be discarded.

Turning back to Step 406, the keyframes are extracted at a rate of oneper subshot and result in the identification of the “most still” imageframe in the subshot (the frame with the lowest stillness value)—asdepicted in FIG. 5, discussed above. In some embodiments, suchextraction can be based on comparing the stillness value (or score) ofkeyframes to a threshold associated with motion energy, and selectingthose keyframes that satisfy the threshold. This results in producingsharp keyframes that depict video content which can be viable candidatesfor a thumbnail.

In Step 408, a thumbnail is selected based on the remaining frames fromthe keyframe extraction (Step 406) that was performed on the remainingframes from the frame filtering (Step 404). Step 408 is performed by thethumbnail selection module 308. As discussed herein, Step 408 involvesthe thumbnail selection module 308 selecting a frame based on criteriaincluding: “relevance” and “attractiveness” (or high-quality).

In some embodiments, as discussed herein, Step 408 involves determininga frame's relevance to the content or context of the video file, thendetermining the frame's quality (or attractiveness). In someembodiments, such determination can be performed in the reverse order,and in some embodiments, such determination can be performed as a linearcombination of the relevance and quality, where the values of relevanceand quality are weighted to account for their determined values.

According to some embodiments, Step 408 first begins with thedetermination of how representative the content of a frame is to thecontent of the video—i.e., the relevance of the frame. In someembodiments, the thumbnail selection module 308 performs clusteringanalysis on the frames remaining after Step 406, and measures therelevance of each keyframe based on the size of the clusters. That is,in some embodiments, the keyframes are clustered using the k-meansalgorithm with a gap statistic technique in order to automatically findthe optimal number of clusters. Usage of the gap statistic techniqueinvolves comparing a change in within-cluster dispersion with thatresult under an appropriate reference null distribution. From theoptimal clustering result, thumbnail candidates can be generated, oneper cluster, by selecting a frame with the highest aesthetic score.Using the determined scores and cluster size, the candidates can beranked, as in Step 410, by the thumbnail selection module 308.

Step 408 further involves determining how visually attractive thecontent of the frames are—i.e., the quality of the frame. In order todetermine the quality of a frame, computational aesthetics techniquesare utilized by the thumbnail selection module 308, where two versionsof the module 308 can be implemented: unsupervised and supervised. Insome embodiments, the unsupervised technique is based on a heuristicthat uses the stillness score, discussed above, as the module 308selects a thumbnail from each cluster by finding the smallest framedifference value within each cluster.

In some embodiments, the supervised technique performed by the module308 is based on computational aesthetics framework techniques thatassess the photographic attractiveness of images. Here, a set of visualfeatures designed to capture various aesthetic properties are extracted,and a random forest regression model is trained on a set of imagesannotated with subjective aesthetic scores. This methodology is used toassign a quality score to each keyframe. Using this score, thumbnailscan be selected, one per cluster, by finding the highest scoring qualityscore.

With the above framework established, the discussion will turn to adetailed description of the supervised model for frame quality (orattractiveness) scoring. In some embodiments, a 52-dimensional vector ofvisual features can be constructed by the thumbnail selection module 308which captures a set of visual aesthetic properties of a frame(s). As anexample of such properties, the below table provides a view of the typeof properties, their features, their dimensional value (“Dim”), and adescription of each property:

Feature Dim Description Color Contrast 1 Ratio between luminance rangeand average luminance. HSV 3 Average on each channel of Hue, Saturation,and Brightness. HSV 3 Average HSV computed on the central (Central)quadrant of the image. HSV 20 Histograms of HSV channels with 12,Histograms 3, and 5 bins. HSV 3 Standard deviation of the HSV Contrastshistograms. Pleasure, 3 Linear combination of HSV values Arousal,according to visual perception statistics. Dominance Texture GLCM 4Entropy, Energy, Contrast, and Homo- geneity of the Gray-LevelCo-Occurrence Matrix. Basic Contrast 1 The l₂ distance between anoriginal Quality Balance image and contrast-equalized image. Exposure 1Absolute value of the skewness of Balance luminance histogram. JPEG 1No-reference quality estimation Quality algorithm. Sharpness 1 Sum ofimage pixels filtered with Sobel masks. Compo- Presence of 9 Averagesaliency on 9 image sub-regions. sition Objects Uniqueness 1 The l₂distance between image spectrum and average spectrum of natural images.Symmetry 1 Difference between HOG feature vectors of left/right imagehalves.

“Color” or color distribution is perhaps one of the most closely relatedproperties to visual aesthetics. To evaluate the color composition andrendering, module 308 computes the following features: a Contrast metricbased on luminance values; a set of Hue, Saturation, Brightness (HSV)statistics, including the Average HSV value for the whole image; theCentral Average HSV value for the image central quadrant; the HSV ColorHistograms, obtained by quantizing the values of HSV channels; thePleasure, Arousal, Dominance statistics; and the HSV Contrasts (e.g.,the standard deviation of the HSV histograms). In some embodiments, suchcomputation is performed by the module 308 using any one of the aboveknown or to be known techniques for determining and calculatingparameter values of content in a video frame, as discussed above—forexample, using a histogram, vector analysis, neural networks, and thelike.

With reference to the “texture,” an image frame's content texture can bedetermined by extracting the features of content, namely the Entropy,Energy, Homogeneity, Contrast of the Gray-Level Co-occurrence Matrix(GLCM), and the like, using any known or to be known statistical andstructural technique for determining texture of an image, as discussedabove.

“Quality” can be determined in accordance with four basic image qualitymetrics that capture the level of degradation on video frames, typicallycaused by post-processing and compression: Contrast Balance, computed asthe l₂ distance between the original frame and its contrast-normalizedversion after luminance histogram equalization; the Exposure Balance,computed as the skewness of the luminance histogram; a JPEG Qualitymetric based on the presence of JPEG blocking artifacts, and the globalSharpness of an image within a frame.

“Composition” can be described with reference to a scenecomposition—that is, the arrangement of objects in an image frame, whichcan be determined by the module 308 analyzing the distribution of thespectral saliency across 3×3 image subdivisions, thereby capturing byhow much the image follows the golden ratio associated with, forexample, the Rule of Thirds. In some embodiments, module 308 computesthe Symmetry feature, which captures the left-right symmetry of animage, and the Uniqueness feature, which captures the originality of theimage composition.

According to some embodiments, the thumbnail selection module 308 can betrained to score frame aesthetics. In some embodiments, for example, theAesthetic Visual Analysis (AVA) database can be employed. For example,an original dataset contains around 250,000 images with aesthetic,semantic and style labels. The dataset contains images and theircorresponding scores downloaded from a photo contest websiteDPChallenge.com. In that website, professional/aspiring photographersget their photos voted by other photographers in terms of their quality,on a 10-point Likert scale. The aggregation of votes becomes theaesthetic score in the AVA dataset.

Accordingly, the thumbnail engine 300 can implement a supervisedframework, as discussed above, to predict the aesthetic quality of videoframes using a random forest regression model. In some embodiments, inorder to perform such prediction, the module 308 of engine 300 optimizesthe number of trees applied in such model via 5-fold cross validation.In other words, the random forest regression model is trained based onfour splits (80% of the images) and tested on the remaining splits(e.g., 20%), and repeated five times. The number of trees can be variedfrom 1 to 200, and as a result, the setting of the number to 100consistently provides results satisfying a quality and relevancethreshold. The average Mean Squared Error obtained from such analysiswas 0.36 with a Multiple Correlation Coefficient (Pearson's correlationbetween predicted and real scores) of 0.49. Accordingly, such supervisedmodeling of the module 308 can be applied in Step 408 in order tocompute the aesthetic score of all the extracted keyframes.

Continuing with Process 400, Step 412 involves the thumbnail selectionmodule 308 automatically selecting a top ranked thumbnail candidate orset of candidates satisfying a threshold (where the ranking is performedin accordance with Steps 408-410). In Step 414, the selected thumbnailcandidate can then be displayed in place of the input video (from Step402). That is, when a candidate frame is selected, thumbnail selectionmodule 308 can generate a thumbnail image based on the content of theselected frame. Such thumbnail generation from a video frame can beperformed using any known or to be known thumbnail or image generationsoftware, such as, for example, a batch processing program that convertsa frame to a thumbnail image. Step 414 can be performed by the thumbnailselection module 308. As discussed above, the thumbnail, as understoodby those of skill in the art, can take any form, including a stillimage, or a short-form motion image (e.g., a GIF). The thumbnail isinteractive, and enables a user to view the video content of the inputvideo upon interaction with the selected and displayed thumbnail.

By way of a non-limiting example of Process 400, an input video isuploaded to a user's Tumblr® page. Upon receiving the video, Steps404-414 can be performed by the thumbnail engine 300, as discussedabove. The video file is parsed and the frames of the video areidentified as a set of frames. These frames are first subject to framefiltering where the transition frames and the low quality frames arediscarded from the frame set. The remaining frames are then subject tokeyframe extraction where duplicate frames are removed from the framesremaining in the set. The remaining frames of this set are then analyzedbased on their relevance to the content or context of the video (e.g.,how much does the content depicted in a frame correspond to the contentof the video as a whole), and based on how aesthetically pleasing issuch content (e.g., is the content “high-quality,” for example, highresolution, high pixel count and/or high sharpness, and the like). Insome embodiments, the frame with the highest relevance and quality isselected, and a thumbnail is generated from the selected frame, where itis displayed on the Tumblr® page. In some embodiments, the remainingframes can be ranked, where at least one frame from the ranked set isselected for thumbnail generation.

Thus, the disclosed subject matter provides systems and methods forautomatically selecting highly relevant and high-quality image frames ascontent to be displayed within a generated thumbnail image. As discussedabove, the disclosed systems and methods identify attractive thumbnailsby analyzing various visual quality and aesthetic metrics, and perform aclustering analysis to determine the relevance to video content, thusmaking the resulting thumbnails more representative of the video.

FIG. 6 is a work flow example 600 for serving relevant digital mediacontent associated with advertisements (e.g., digital advertisementcontent) based on the information associated with the identified media(or content), as discussed above in relation to FIGS. 3-5. Suchinformation, referred to as “thumbnail information” for referencepurposes only, can include, but is not limited to, analyzed information(i.e., information associated with and/or derived from the stored videosand/or videos' frames), the identity, context and/or type of mediacontent being rendered and/or provided to a user as a thumbnail image,the content of such media, and the like, and/or some combinationthereof.

As discussed above, reference to an “advertisement” should be understoodto include, but not be limited to, digital media content that providesinformation provided by another user, service, third party, entity, andthe like. Such digital ad content can include any type of known or to beknown media renderable by a computing device, including, but not limitedto, video, text, audio, images, and/or any other type of known or to beknown multi-media. In some embodiments, the digital ad content can beformatted as hyperlinked multi-media content that provides deep-linkingfeatures and/or capabilities. Therefore, while the content is referredas an advertisement, it is still a digital media item that is renderableby a computing device, and such digital media item comprises contentrelaying promotional content provided by a network associated thirdparty.

By way of a non-limiting example, work flow 600 includes a user visitinganother user's blog page such as for example a Tumblr® page and beingpresented a thumbnail image for a video that corresponds to highlightclips of last night's baseball game. Based on such information, the usermay be provided with digital ad content related to special promotionsprovided by Major League Baseball® (MLB), such as, for example,promotional deals for purchasing tickets to the next game or purchasingMLB apparel.

In Step 602, thumbnail information is identified. As discussed above,the thumbnail information can be based any of the information formprocesses outlined above with respect to FIGS. 3-5. For purposes of thisdisclosure, Process 600 will refer to single provided/identified contentobject (e.g., a thumbnail image determined from a video file) as thebasis for serving a digital advertisement(s); however, it should not beconstrued as limiting, as any number of thumbnails, identified contentitems, and/or quantities of information related to thumbnails, videosand/or thumbnail generation can form such basis, without departing fromthe scope of the instant disclosure.

In Step 604, a context is determined based on the identified thumbnailinformation. This context forms a basis for serving advertisementsrelated to the thumbnail information. In some embodiments, the contextcan be determined by determining a category which the thumbnailinformation of Step 602 represents. For example, the category can berelated to the content type of the media being represented by thethumbnail. In some embodiments, the identification of the context fromStep 604 can occur before, during and/or after the analysis detailedabove with respect to Process 400, or some combination thereof.

In Step 606, the context (e.g., content/context data) is communicated(or shared) with an advertisement platform comprising an advertisementserver 130 and ad database. Upon receipt of the context, theadvertisement server 130 performs a search for a relevant advertisementwithin the associated ad database. The search for an advertisement isbased at least on the identified context.

In Step 608, the advertisement server 130 searches the ad database for adigital advertisement(s) that matches the identified context. In Step610, an advertisement is selected (or retrieved) based on the results ofStep 608. In some embodiments, the selected advertisement can bemodified to conform to attributes of the page, message or method uponwhich the advertisement will be displayed, and/or to the applicationand/or device for which it will be displayed. In some embodiments, theselected advertisement is shared or communicated via the application theuser is utilizing to view, search for and/or render the media (e.g.,view the thumbnail or render the video, for example). Step 612. In someembodiments, the selected advertisement is sent directly to each user'scomputing device. In some embodiments, the selected advertisement isdisplayed in conjunction with the rendered and/or identified mediaassociated with the displayed thumbnail on the user's device and/orwithin the application being used to view and/or render the media.

As shown in FIG. 7, internal architecture 700 of a computing device(s),computing system, computing platform and the like includes one or moreprocessing units, processors, or processing cores, (also referred toherein as CPUs) 712, which interface with at least one computer bus 702.Also interfacing with computer bus 702 are computer-readable medium, ormedia, 706, network interface 714, memory 704, e.g., random accessmemory (RAM), run-time transient memory, read only memory (ROM), mediadisk interface 708 and/or media disk drive interface 720 as an interfacefor a drive that can read and/or write to media including removablemedia such as floppy, CD-ROM, DVD, media, display interface 710 asinterface for a monitor or other display device, keyboard interface 716as interface for a keyboard, pointing device interface 718 as aninterface for a mouse or other pointing device, and miscellaneous otherinterfaces 722 not shown individually, such as parallel and serial portinterfaces and a universal serial bus (USB) interface.

Memory 704 interfaces with computer bus 702 so as to provide informationstored in memory 704 to CPU 712 during execution of software programssuch as an operating system, application programs, device drivers, andsoftware modules that comprise program code, and/or computer executableprocess steps, incorporating functionality described herein, e.g., oneor more of process flows described herein. CPU 712 first loads computerexecutable process steps from storage, e.g., memory 704, computerreadable storage medium/media 706, removable media drive, and/or otherstorage device. CPU 712 can then execute the stored process steps inorder to execute the loaded computer-executable process steps. Storeddata, e.g., data stored by a storage device, can be accessed by CPU 712during the execution of computer-executable process steps.

Persistent storage, e.g., medium/media 706, can be used to store anoperating system and one or more application programs. Persistentstorage can also be used to store device drivers, such as one or more ofa digital camera driver, monitor driver, printer driver, scanner driver,or other device drivers, web pages, content files, playlists and otherfiles. Persistent storage can further include program modules and datafiles used to implement one or more embodiments of the presentdisclosure, e.g., listing selection module(s), targeting informationcollection module(s), and listing notification module(s), thefunctionality and use of which in the implementation of the presentdisclosure are discussed in detail herein.

Network link 728 typically provides information communication usingtransmission media through one or more networks to other devices thatuse or process the information. For example, network link 728 mayprovide a connection through local network 724 to a host computer 726 orto equipment operated by a Network or Internet Service Provider (ISP)730. ISP equipment in turn provides data communication services throughthe public, worldwide packet-switching communication network of networksnow commonly referred to as the Internet 732.

A computer called a server host 734 connected to the Internet 732 hostsa process that provides a service in response to information receivedover the Internet 732. For example, server host 734 hosts a process thatprovides information representing image and/or video data forpresentation at display 710. It is contemplated that the components ofsystem 700 can be deployed in various configurations within othercomputer systems, e.g., host and server.

At least some embodiments of the present disclosure are related to theuse of computer system 700 for implementing some or all of thetechniques described herein. According to one embodiment, thosetechniques are performed by computer system 700 in response toprocessing unit 712 executing one or more sequences of one or moreprocessor instructions contained in memory 704. Such instructions, alsocalled computer instructions, software and program code, may be readinto memory 704 from another computer-readable medium 706 such asstorage device or network link. Execution of the sequences ofinstructions contained in memory 704 causes processing unit 712 toperform one or more of the method steps described herein. In alternativeembodiments, hardware, such as ASIC, may be used in place of or incombination with software. Thus, embodiments of the present disclosureare not limited to any specific combination of hardware and software,unless otherwise explicitly stated herein.

The signals transmitted over network link and other networks throughcommunications interface, carry information to and from computer system700. Computer system 700 can send and receive information, includingprogram code, through the networks, among others, through network linkand communications interface. In an example using the Internet, a serverhost transmits program code for a particular application, requested by amessage sent from computer, through Internet, ISP equipment, localnetwork and communications interface. The received code may be executedby processor 702 as it is received, or may be stored in memory 704 or instorage device or other non-volatile storage for later execution, orboth.

For the purposes of this disclosure a module is a software, hardware, orfirmware (or combinations thereof) system, process or functionality, orcomponent thereof, that performs or facilitates the processes, features,and/or functions described herein (with or without human interaction oraugmentation). A module can include sub-modules. Software components ofa module may be stored on a computer readable medium for execution by aprocessor. Modules may be integral to one or more servers, or be loadedand executed by one or more servers. One or more modules may be groupedinto an engine or an application.

For the purposes of this disclosure the term “user”, “subscriber”“consumer” or “customer” should be understood to refer to a user of anapplication or applications as described herein and/or a consumer ofdata supplied by a data provider. By way of example, and not limitation,the term “user” or “subscriber” can refer to a person who receives dataprovided by the data or service provider over the Internet in a browsersession, or can refer to an automated software application whichreceives the data and stores or processes the data.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client level or server level or both. In thisregard, any number of the features of the different embodimentsdescribed herein may be combined into single or multiple embodiments,and alternate embodiments having fewer than, or more than, all of thefeatures described herein are possible.

Functionality may also be, in whole or in part, distributed amongmultiple components, in manners now known or to become known. Thus,myriad software/hardware/firmware combinations are possible in achievingthe functions, features, interfaces and preferences described herein.Moreover, the scope of the present disclosure covers conventionallyknown manners for carrying out the described features and functions andinterfaces, as well as those variations and modifications that may bemade to the hardware or software or firmware components described hereinas would be understood by those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described asflowcharts in this disclosure are provided by way of example in order toprovide a more complete understanding of the technology. The disclosedmethods are not limited to the operations and logical flow presentedherein. Alternative embodiments are contemplated in which the order ofthe various operations is altered and in which sub-operations describedas being part of a larger operation are performed independently.

While various embodiments have been described for purposes of thisdisclosure, such embodiments should not be deemed to limit the teachingof this disclosure to those embodiments. Various changes andmodifications may be made to the elements and operations described aboveto obtain a result that remains within the scope of the systems andprocesses described in this disclosure.

What is claimed is:
 1. A method comprising: identifying, via a computing device, a video, said video comprising a plurality of video frames each displaying an image; in response to identifying said video, automatically, via the computing device, performing frame filtering on the video by parsing, via the computing device, each of said video frames and identifying, based on said parsing, content of each image of each frame, said frame filtering further comprising determining, based on the content of each frame, a type of each frame, said frame filtering comprising identifying a first set of frames from said plurality of video frames based on said type of each frame; analyzing, via the computing device, said first set of frames by applying de-duplication software, said application comprising the computing device executing said de-duplication software, and based on said execution of the de-duplication software, determining duplicate frames within said first set of frames, said analysis further comprising, based on said duplicate frame determination, discarding said duplicate frames from said first set; computing, via the computing device, a stillness value for the remaining frames in the first set, said stillness value comprising an indication of motion energy present within content of the frames remaining in the first set; identifying, via the computing device, a second set of frames by extracting, via the computing device, keyframes from the remaining frames in said first set based on said stillness value, said extracted keyframes having a stillness value satisfying a threshold for motion energy, said extracted keyframes being a non-redundant subset of the video frames; determining, via the computing device, a relevance value for each frame in the second set, said relevance value determination comprising analyzing, via the computing device, aesthetic features of each frame in the second set and determining, based on said analysis, an aesthetic value for each frame, said determination further comprising clustering the frames in the second set based on a statistical gap between aesthetic values of the frames, and identifying a third frame set by selecting a frame from each cluster that has a highest aesthetic score; determining, via the computing device, a quality value of the frames in the third set based on a ranking of the aesthetic scores in the third set; and generating, via the computing device, a thumbnail image by selecting a frame in the third set having the highest aesthetic score, said thumbnail image comprising content of said selected frame.
 2. The method of claim 1, further comprising: automatically displaying said generated thumbnail image on a page as a representation of the video.
 3. The method of claim 1, wherein said frame filtering further comprises: identifying frames within said plurality that have a type associated with at least one of a low-quality frame or a transition frame; and generating said first set of frames while excluding said low-quality and transition frames, wherein said first set of frames comprises all of the video frames except those identified as low-quality or transition frames.
 4. The method of claim 3, wherein said low-quality frames comprise content that is dark, blurry or uniform-colored.
 5. The method of claim 4, wherein said dark content is determined by computing a relative luminance, said luminance computation comprising: Luminance(I _(rgb))=0.2126Ir+0.7152I _(g)+0.0722I _(b), wherein rgb refers to RGB color space, and wherein said computed luminance is subject to a thresholding analysis based on an empirically selected value.
 6. The method of claim 4, wherein said blurry content is determined by computing a sharpness value, said sharpness computation comprising: Sharpness(I _(gray))=√((Δ_(x) Igray)²+(Δ_(y) Igray)²)  (Eq. 2).
 7. The method of claim 4, wherein said uniform-colored content is determined by frame filtering steps comprising: computing a normalized intensity histogram for image content of a frame resulting in values of intensity; sorting said values in descending order; computing a cumulative distribution at top percentage bins; and thresholding said cumulative distribution based on an empirically selected value.
 8. The method of claim 3, wherein said transition frames are determined based on said computing device executing software defined by a shot boundary detection algorithm which determines transition frames from its input.
 9. The method of claim 1, wherein said second set of frames is further based on an analysis comprising: determining a set of shots from said first set of frames by applying software defined by a k-means algorithm on said first set of frames; clustering the frames from the first set based on said k-means determination; and analyzing said clustered frames and identifying a continuous block of frames within a single cluster; wherein said stillness value is determined in accordance with said continuous block of frames.
 10. The method of claim 1, wherein said quality value determination is based on the stillness value of each frame in the third set.
 11. The method of claim 1, wherein said quality value determination further comprises: extracting said aesthetic features of each frame in the third set; and applying software defined by a random forest regression model and determining a quality score for each frame, wherein said selected frame has a highest quality score.
 12. The method of claim 1, further comprising: determining a context of the thumbnail image, said context being in accordance with said content of said selected frame; causing communication, over the network, of said context to an advertisement platform to obtain digital advertisement content associated with said context; and displaying a digital content item comprising said digital advertisement content in accordance with said thumbnail image.
 13. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a processor associated with a computing device, performs a method comprising: identifying, via the computing device, a video, said video comprising a plurality of video frames each displaying an image; in response to identifying said video, automatically, via the computing device, performing frame filtering on the video by parsing, via the computing device, each of said video frames and identifying, based on said parsing, content of each image of each frame, said frame filtering further comprising determining, based on the content of each frame, a type of each frame, said frame filtering comprising identifying a first set of frames from said plurality of video frames based on said type of each frame; analyzing, via the computing device, said first set of frames by applying de-duplication software, said application comprising the computing device executing said de-duplication software, and based on said execution of the de-duplication software, determining duplicate frames within said first set of frames, said analysis further comprising, based on said duplicate frame determination, discarding said duplicate frames from said first set; computing, via the computing device, a stillness value for the remaining frames in the first set, said stillness value comprising an indication of motion energy present within content of the frames remaining in the first set; identifying, via the computing device, a second set of frames by extracting, via the computing device, keyframes from the remaining frames in said first set based on said stillness value, said extracted keyframes having a stillness value satisfying a threshold for motion energy, said extracted keyframes being a non-redundant subset of the video frames; determining, via the computing device, a relevance value for each frame in the second set, said relevance value determination comprising analyzing, via the computing device, aesthetic features of each frame in the second set and determining, based on said analysis, an aesthetic value for each frame, said determination further comprising clustering the frames in the second set based on a statistical gap between aesthetic values of the frames, and identifying a third frame set by selecting a frame from each cluster that has a highest aesthetic score; determining, via the computing device, a quality value of the frames in the third set based on a ranking of the aesthetic scores in the third set; generating, via the computing device, a thumbnail image by selecting a frame in the third set having the highest aesthetic score, said thumbnail image comprising content of said selected frame; and automatically causing display, via the computing device, of said generated thumbnail image on a page as a representation of the video.
 14. The non-transitory computer-readable storage medium of claim 13, wherein said frame filtering further comprises: identifying frames within said plurality that have a type associated with at least one of a low-quality frame or a transition frame; and generating said first set of frames while excluding said low-quality and transition frames, wherein said first set of frames comprises all of the video frames except those identified as low-quality or transition frames.
 15. The non-transitory computer-readable storage medium of claim 14, wherein said low-quality frames comprise content that is dark, blurry or uniform-colored; wherein said dark content is determined by computing a relative luminance, said luminance computation comprising: Luminance(I _(rgb))=0.2126Ir+0.7152I _(g)+0.0722I _(b)  (Eq. 1), wherein rgb refers to RGB color space, and wherein said computed luminance is subject to a thresholding analysis based on an empirically selected value; wherein said blurry content is determined by computing a sharpness value, said sharpness computation comprising: Sharpness(I _(gray))=√((Δ_(x) Igray)²+(Δ_(y) Igray)²); and wherein said uniform-colored content is determined by frame filtering steps comprising: computing a normalized intensity histogram for image content of a frame resulting in values of intensity; sorting said values in descending order; computing a cumulative distribution at top percentage bins; and thresholding said cumulative distribution based on an empirically selected value.
 16. The non-transitory computer-readable storage medium of claim 14, wherein said transition frames are determined based on said computing device executing software defined by a shot boundary detection algorithm which determines transition frames from its input.
 17. The non-transitory computer-readable storage medium of claim 13, wherein said second set of frames is further based on an analysis comprising: determining a set of shots from said first set of frames by applying software defined by a k-means algorithm on said first set of frames; clustering the frames from the first set based on said k-means determination; and analyzing said clustered frames and identifying a continuous block of frames within a single cluster; wherein said stillness value is determined in accordance with said continuous block of frames.
 18. The non-transitory computer-readable storage medium of claim 13, wherein said quality value determination is based on the stillness value of each frame in the third set.
 19. The non-transitory computer-readable storage medium of claim 13, wherein said quality value determination further comprises: extracting said aesthetic features of each frame in the third set; and applying software defined by a random forest regression model and determining a quality score for each frame, wherein said selected frame has a highest quality score.
 20. A system comprising: a processor; a non-transitory computer-readable storage medium for tangibly storing thereon program logic for execution by the processor, the program logic comprising: logic executed by the processor for identifying a video, said video comprising a plurality of video frames each displaying an image; logic executed by the processor for, in response to identifying said video, automatically performing frame filtering on the video by parsing each of said video frames and identifying, based on said parsing, content of each image of each frame, said frame filtering further comprising determining, based on the content of each frame, a type of each frame, said frame filtering comprising identifying a first set of frames from said plurality of video frames based on said type of each frame; logic executed by the processor for analyzing said first set of frames by applying de-duplication software, said application of the de-duplication software comprising executing said de-duplication software, and determining, based on said execution of the de-duplication software, duplicate frames within said first set of frames, said analysis further comprising, based on said duplicate frame determination, discarding said duplicate frames from said first set; logic executed by the processor for computing a stillness value for the remaining frames in the first set, said stillness value comprising an indication of motion energy present within content of the frames remaining in the first set; logic executed by the processor for identifying a second set of frames by extracting keyframes from the remaining frames in said first set based on said stillness value, said extracted keyframes having a stillness value satisfying a threshold for motion energy, said extracted keyframes being a non-redundant subset of the video frames; logic executed by the processor for determining a relevance value for each frame in the second set, said relevance value determination comprising analyzing aesthetic features of each frame in the second set and determining, based on said analysis, an aesthetic value for each frame, said determination further comprising clustering the frames in the second set based on a statistical gap between aesthetic values of the frames, and identifying a third frame set by selecting a frame from each cluster that has a highest aesthetic score; logic executed by the processor for determining a quality value of the frames in the third set based on a ranking of the aesthetic scores in the third set; logic executed by the processor for generating a thumbnail image by selecting a frame in the third set having the highest aesthetic score, said thumbnail image comprising content of said selected frame; and logic executed by the processor for automatically displaying said generated thumbnail image on a page as a representation of the video. 